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Catherine Roy and I, as members of the Multi-Stakeholder Forum to Open Government, Government of Canada, and as Chairs and Co-chairs of the MSF commitment on Data for Results: Disability, submitted the following to the Government of Canada commitments to the international Open Government Partnership. This would be for the next Action Plan For Canada.

Catherine and I officially met with Government of Canada representatives, beyond the MSF officials, on this important commitment, on June 28, 2021. We had requested to meet, as part of the engagement process, with officials from Employment and Social Development Canada (ESDC), and the Canadian Accessibility Standards Development Organization. They unfortunately did not attend the civil society consulation. We did meet with officials from Statistics Canada; the Office of the Deputy Minister of Public Service Accessibility and the Canada Revenue Agency in addition to our usual MSF Open Government Team at the Treasury Board Secretariat of Canada.

The Civil Society Commitments we submitted and discussed, were developed in consultation with civil society disabled people’s organizations, experts and advocates with lived experience. It was the first time that such a group had been brought together to specifically discuss data about disabled people in Canada.

After the meeting, the civil society representatives met, and recommended an additional commitment and refined the 7 point Data For Results on Disability Commitment as posted below, that was presented to government. We also reached out to those who sent their regrets for their input. It was great to have these key experts, people with lived experience and disabled people civil society organizations at the table. This was no small feat given the context of the pandemic and the front line priorities these organizations and people have.

We then formally asked that these Refined Commitments be shared with government of Canada counterparts in an email. In that correspondence we offered to look at a draft, and or to discuss ideas. It is conventional to discuss draft commitments.

Unfortunately, the Government of Canada actors responsible for this commitment did not discuss their proposed commitments prior to the public consultation process, and as you can see here, the Government’s proposal does not resemble what civil society actors requested. The Government’s draft commitment does however resemble the 2021 Federal Budget announcement on disagregated data, and new additions that just came in this afternoon, are a list of work government was already doing, thus these do not consitutue new commitments. It is great that this work is beind done, but these do not address the request that was made. (see below Civil Society Commitments 1 & 2)

As per the process, we formally submitted our disapointment to the Government of Canada proposal in an email, and we did so today again, and we have submitted comments as part of the online consultation process which you can find here and I have posted images below. We hope that this part of the process works, and that our questions and comments will be responded to. You can submit comments to!

We also hope that we will be able to work together on addressing how disabled people are unseen and generally unacounted for in government statistics, government data and administrative data, let alone open data, and that when they are, they are often misclassified or overly surveyed and audited in programs and taxation as they are often identified as outliers. Also, that data will be collected about topics and issues of importance to disabled people, in the spirit of Nothing about us with out us!

We believe the Government operated with good intentions, and we hope to be able to work together to fulfill the requests from civil society and, that our commitments will become policy and that we can have meaningful dialogue to co-create open government policy as per the process.


Data for Results: Data about People with Disabilities

MSF Civil Society actors identified a significant data deficit regarding the living conditions and well-being of people with disabilities in Canada. These historical and systemic inequities became more visible and problematic throughout COVID-19 pandemic response. Existing data are outdated, insufficient, difficult to find and do not address systemic issues.

People with disabilities are counted as part of the Employment Equity Act in terms of employment in the Federal Government, there is a new Act to ensure a barrier-free Canada, which has led to the creation of a Canadian Accessibility Standards Development Organization (CASDO), the Canadian Survey on Disability (CSD) targets people who are 15 years and over on Census day. These are to be lauded, but there remains an absence of data about the living conditions of this group of people in terms of individual residences and most especially collective dwellings which more often than not involves state support.  There is also a lack of any national understanding of how this group of people in Canada are faring and when in collective dwellings, how much freedom is afforded in terms of movement. This would be the responsibility of Statistics Canada, the CASDO, and the Public Health Agency of Canada (PHAC).

Currently there is no one statistical unit responsible for the collection of data, surveys and administrative data about people with disabilities. There is no national dataset nor a typology of collective residences and collective care facilities such as group homes, home care, and respite care. There are no unique classification systems, and the General Social Survey and the Census do not survey people with disabilities where they live, missing those who live in institutions and custodial housing.  We also know little of the employment, education, health, and economic status and wellbeing of this group of Canadians. This is a first step to knowing more, and to inform policies, programs and services. Internationally this group of people are underrepresented, invisible and unseen in Open Data priorities. We hope to also address this international Open Government and Open Data gap.

Multi-Stakeholder Forum Civil Society Commitment Requests on the topic of Open Data For Results – Disability:

Data for Results – Disability Civil Society Commitment 1:

• We would like the following 7 step Data For Results process on Disability commitment:

1. Disability Data Inventory – Conduct a cross government of Canada inventory to identify data, surveys, administrative data, program and service data, models, crowdsourcing data collection programs about disabled people, including their social and material lived experience. This would include methodological guides, data dictionaries, classification systems & taxonomies, standards, etc. as well as data owners, and how the data are used in decision making and reporting.
2. Analyze – Analyze the inventory and identify data gaps and issues with disability civil society actors and experts in and outside of government. This includes identifying risks related to automated decision making, validity of the data, assessment of proxies, bias and models of disability. Also, with great attention to the potential ramifications of data to the lives of disabled people i.e. disabled people are often audited more by the CRA as their lives do not follow traditional life courses.
3. Publish – The results of the inventory and later the analysis of the inventory on the Open Government Website and publish any data that can be made open in the open data portal with accompanying documentation. This can be a type of clearinghouse on disability, including laws, regulations, official reporting, etc.
4. Collect – Develop procedures to ameliorate existing datasets, and work with Civil Society actors and experts in and outside of government to produce and collect new data with responsible authorities, keeping in mind the risks discussed earlier.
5. Publish – New, current, and historical existing data on the Government of Canada Open Data portal and also, for those data that cannot be open data, these ought to be listed with details and a contacting authority.
6. Act – Data for Results is about informing public policy, programs and services to address inequities and the social and material conditions of disabled people, to assess impact and outcomes of regulation, to mitigate ableism and to improve data collection, dissemination and processes including impact and oversight, while also creating affirmative indicators related to participation in Canadian life and the workforce. Impact metrics will be required to monitor progress.
7. Report Results – Report policy, program and service improvements and outcomes.

Data for Results – Disability Civil Society Commitment 2:

Considering the importance of the availability of data designed according to and compatible with the systemic social model of disability to ensure the monitoring of the Convention on the Rights of Persons with Disabilities (UNCRPD) ratified by Canada, it is recommended to include the Human Rights Commission of Canada in any discussion related to the government’s commitment to open data in the area of disability. In addition, the lead Canadian organization in monitoring the CRPD with civil society partners is the British Columbia Aboriginal Network on Disability Society (BCANDS) crpd@bcands.bc.ca “

Image of the Government of Canada Response to our Initial Request and what is currently out for consultation:

Open Government Commitment by Government of Canada

Update – Wereceived notice at 14:00 YOW time, that the following was forgottent in the Government Consulation Document

Just added list of datasets!

My submitted comments about this oversight are as follows:

“The addition of 3.7 onward, just now on a Friday afternoon before an election, is great, but these do not constitute Open Government Commitments, as these are the usual business of Government, some of the surveys should not have been cancelled in the first place, and these were things Government had already committed to. We are glad that you are going to do them. Also they have just been added on the last days of the consultation.  In addition, some of these surveys do not sample disabled people living in collective dwellings such as domiciliary institutions, the living arrangements for many disabled people, which means it will miss many resulting in a gross under count and under representatio, especially people with developmental disabilities and potentially the elderly with disabilities who live in elder care homes, and any other disabled people relegated to prisons because of inadequate mental institutions, and others with developmental disabilities in mental institutions because of a lack of other forms of care.

We of course, continue to hope that our group of civil society actors will be consulted and an advisory board will be constituted with the people and organizations who helped shape and endorse the civil society submitted commitments.

Part of the issues identified by civil society, are these types of data gaps in existing counting systems, including a clear lack of typologies and classification systems about foundational issues such as living arrangements and dwelling types. 

Finally, as is the case with Indigenous people, there ought to be systems developed by and with disabled people as the current deficit indicators are not useful, and what of data that are meaningful to disabled people? 

Again, we are most willing to work with you.  And we hope that our call for an inventory, the analysis of the data from that inventory, and an advisory group can be set up in an official capacity so that we do this work together.  We look forward to your thoughts on this.  We have posted our observations and requests here as comments to this document and also here https://www.datalibre.ca/2021/08/13/open-data-for-results-disability/.

Also note, that the groups we worked with as part of this process, expected their contribution in terms of the development of commitments to be enough, they did not expect to have to come back to a website and also have to reiterate what they have already share with you.  Thus we did!

Again, we believe the Government has good intentions, but in the absence of dialogue with us, missed what we were proposing and why. The Open Government process involves meaningful engagement, and new commitments beyond usual government business.  I am sure we will be able to help you formulate a commitment that is new, and one that is meaningful to disabled people in Canada.”

Here’s what we know …

Earlier this month, the Megan Linton shared her research about the missing database of congregate institutions for disabled people. This data gap makes it difficult to trace COVID’s impacts on these vulnerable populations and to accurately prioritize vaccine distribution amidst the phased roll-out. Today (03/01/2021), federal and provincial COVID-19 vaccine roll outs still have yet to include disabled people. 

In the UK—where this data is disaggregated—we know disabled people make up 60% of all COVID deaths, and that disabled people with developmental disabilities are four to six times more likely to die from COVID-19. Canada needs data on where disabled people are living to prioritize them in our vaccination program.  

“…the lack of data is a major hurdle when making decisions, so a nationwide census needs to be undertaken to determine how many people with disabilities live in institutions or semi-institutions in Canada.”

Jonathan Marchand, Président, Coop ASSIST

We need your help to learn more about congregate institutions for disabled people.

On Sunday March 7th, 13:00–16:00, join the Canadian Open Data Society (CODS), GO Open Data (GOOD), and Open North together as part of the Tracing COVID-19 Data Project to “Hack the Data Gap” and make institutions for disabled adults visible. 

If you enjoy hunting for information online and know your way around a spreadsheet, we need you. Orientation and instructions will be provided (though you’ll have to provide your own coffee and donuts).

Join us this Sunday via Zoom to Hack this Data Gap… and together let’s shed light on this issue!

Please register here to participate.

In the News!

Megan Linton (Sociology) the Tracing COVID-19 Data project’s critical disabilities studies expert supported by Kit Chokly (Communications) our data intersectionality expert and designer,  have been leading the charge on making public the data invisility of people living in custodial institutions. The backgrounder is available here, and in essence we are trying to compile, with disability and open data advocates across the country, a foundational dataset so that these folks can be seen in policy and in action.

Megan, in addition to being an up-and-coming scholar, is also a person with the lived experience of a disabled person has been in the news talking about these invisibilities.  Today she was interviewed by CBC’s Alan Neal on All in a Day 

and authored the following article:  Ontario’s hidden institutions Facilities like ‘domiciliary hostels’ are an outdated model of custodial care that violates disabled people’s rights.

The research team is digging for information to compile into a database with the Canadian Open Data Society, GO Open Data, and Open North and several people in the disabled people’s community and volunteers at large.

A work in progress Disabled People’s Database — Invisible Institutions in Ottawa created by the team was also released.

We will have a public crowdsourcing activity on March 6 for International Open Data Day. Stay tuned!

Article written by: Megan Linton, with support from Kit Chokly and Tracey P. Lauriault

Key Facts:

  • There is no national nor provincial dataset / inventory of residential service homes, congregate institutions or custodial housing for disabled people in Canada.
  • The last count, conducted in 2009, of people living in Ontario domiciliary hostels was 4 700.
  • Residents in these homes cannot meet the 2 meter physical distance requirements as their shared rooms and spaces are too small
  • People who live in residential service homes share bedrooms and bathrooms, placing residents in the highest risk category of a COVID-19 outbreak.
  • Adults with intellectual disabilities are more likely to die or experience serious complications from COVID-19 as compared to the rest of the population, in England Disabled people account for 6 in 10 COVID-19 deaths (BBC, Feb. 11, 2021)


Residential service homes are congregate institutions that provide long-term housing to chronically unhoused, older and/or disabled adults over the age of 18. These are institutions, large and small, and based on our preliminary analysis, that serve between 10 and 150 residents. People living in these residences share bathrooms, bedrooms, common spaces and dining rooms. Residential service home standards require that there be at least 0.94 meter spacing between beds in shared bedrooms, 1.39 meters of space in common areas for each resident, and at least one accessible bathroom per institution (City of Ottawa, 2016). In these residences it is not possible to meet the COVID-19 public health recommendations of a minimum of 2 meters between individuals (Public Health Ontario, 2021). 

Under these living conditions it is difficult to control the spread of COVID-19. The recent outbreak at the Edgewood Care Centre in Ottawa is evidence of this. It is a 130 person privately operated institution, where 27% of residents contracted COVID-19 (Linton, 2021; Payne, 2021). These living conditions are unknown to most and are not accounted for as there are no concerted efforts to trace these outbreaks as there is no administration tasked with collecting these data in Canada as the responsibility for these falls under many jurisdictions. There are emerging data collection efforts in the UK and US that have identified that adults with intellectual disabilities are significantly more likely [1] to die from COVID-19 if they get the disease and the larger the size of the institution, the higher the mortality rate (Public Health England, 2020). 

Image of a single room with 2 bedrooms at Watford House in Ottawa. Image of a typical bedroom arrangement. "In shared bedrooms, space should be increased between beds to at least 2 metres apart. If this is not possible, consider different strategies to keep residents apart (e.g., place beds head to foot or foot to foot, using temporary barriers between beds)” (Ministry of Health, 2020) (Image of Bedroom in Watford House Ottawa, 2021)
Image of a typical bedroom arrangement. “In shared bedrooms, space should be increased between beds to at least 2 metres apart. If this is not possible, consider different strategies to keep residents apart (e.g., place beds head to foot or foot to foot, using temporary barriers between beds)” (Ministry of Health, 2020) (Image of Bedroom in Watford House Ottawa, 2021)
Image of a typical communal dining room (Watford House Ottawa, 2021) The image includes several small square tables with 4 wooden chairs around them.
Image of a typical communal dining room (Watford House Ottawa, 2021)

As of 2017, the wait list also known as the Service Registry for residential services for adults with intellectual/developmental disabilities (I/DD), was 15 700 persons (Developmental Services Housing Task Force, 2017). As a result, adults with I/DD are dispersed across a wide-range of congregate institutions, including residential service homes (Hwang et al., 2009), long term care institutions (Ouellette-Kuntz et al., 2017), and psychiatric institutions (Dube, 2016). There has yet to be the collection of disaggregated data on the impacts of COVID-19 on adults with I/DD in Canada (Campanella et al., 2021). 

Example of a typical accessible bathroom, the image includes a sink, handles on the wallk to the shower rooms and a seat  (The Standard stipulates that only 1 accessible bathroom is required, regardless of the number of residents). (Baycrest, 50 residents in shared rooms and 1 bathroom)
Example of a typical bathroom (The Standard stipulates that only 1 accessible bathroom is required, regardless of the number of residents). (Baycrest, 50 residents in shared rooms and 1 bathroom)

The last count – done in 2009 – was 4 700 people living in domiciliary hostels in Ontario

Residential service homes were initially designed for older adults who did not require the same services of long-term care institutions. The most recent analysis was done in 2009—prior to the closure of provincially operated residential institutions for adults with I/DD. Since then, the waiting list for access to residential services for disabled adults has had significant growth (Auditor General, 2014; Auditor General, 2016). In Hwang’s 2009 study, 75% of residents are under 65, 89% have at least one physical disability, 23% have a I/DD diagnosis and 94% are disabled [2] (Hwang et al., 2009). 

Where are the data?

Presently, public health units are not reporting disaggregated data on disability-based congregate institutions, making it difficult to understand the effects of COVID-19. This statistical dearth presents a challenge as we approach an anticipated third-wave in Ontario (Ontario COVID-19 Science Advisory Table, 2021). Further, as Ontario enters phase II of vaccination distribution which identifies congregate institutions as priorities for vaccination, yet there are no comprehensive national or provincial or municipal databases of congregate institutions. These data invisibilities make it difficult to prioritize care and the rollout of the vaccine where there is a high risk of the spread of COVID-19. 

Most residential service homes are regulated by the municipalities in Ontario, and there is no administrative requirement to maintain a central database of these institutions. Further, services and support for adults with I/DD are inter-jurisdictional and inter-ministerial resulting in a significant, ongoing data gap (Lunsky et al., 2013; Dube, 2016). This gap is furthered by the exclusion of residential institutions from the Statistics Canada Census (Durbin et al., 2019, Migdal, 2018). 


Public health data show the greater likelihood of COVID-19 mortality in large congregate institutions. Residential service homes should therefore be a priority in receiving the COVID-19 vaccination.  

Priority should be given to the residents living in these institutions based on the following criteria: 

  1. The size of the institution; 
  2. Residents share small bedrooms; 
  3. The ratio of residents per washroom is high
  4. The age of residents and 
  5. The presence of comorbidities

The Auditor General of Ontario and the Ontario Ombudsman have made the recommendation to collect these data on several occasions (1988; 2014; 2016), and while we wait, people’s living conditions remain unseen, uncounted, invisible and therefore unaccounted for in public health policy.

People living in these types of residence should be prioritized for vaccination as should those who assist them, we need to know where these residences are and how many people live in them. 

Efforts as part of the Tracing-COVID-19 Data project are ongoing to produce and open dataset we are calling Megan’s Database of Canadian Custodial Institutions for Disabled People. This is but a start, and we call upon governments federal, provincial/territorial and municipal to help with this endeavour.

[1]  Research from the UK identifies that adults with developmental disabilities are four to six times more likely to die from COVID-19 than other individuals (Public Health England, 2020). Research from the US found they were three times as likely to die from COVID-19 (FAIR Health, 2020).

[2] As 94% of residents receive Ontario Disability Support Program funding (Hwang et al., 2009).


The pandemic has revealed that foundational datasets about specific Canadian populations are missing, including data on the number of disabled people currently living in custodial institutions and the state of their living conditions. This briefing was produced by Megan Linton as part of her ongoing research on disability and institutionalization in Canada, and the current data research is being conducted as part of the Tracing COVID-19 Data Project at Carleton University. 

Article written by: Amanda Hunter & Tracey P. Lauriault

The Organisation for Economic Co-operation and Development (OECD) recently published a policy response to COVID-19 in which they suggest that open science, and the policies & standards that support it, can accelerate the health, social, and economic responses to the virus as barriers to information access are eliminated.

As the first in a series of blog posts about Open Science (OS) and FAIR principles in Canada, here we highlight the key role open science plays in communicating and disseminating official COVID-19 research and public health data before assessing if official COVID-19 reporting in Canada adheres to OS principles.

In a next post, we will analyze official COVID-19 reporting in Canada to assess whether or not these follow Open Science, FAIR principles, and the Open Data Charter in the sharing of COVID-19 data.

What is Open Science?

The OECD Open Science program states that the benefits of open science is that it promotes a more accurate verification of scientific results, reduces duplication, increases productivity, and promotes trust in science.


Open science (OS) is a movement, a practice and a policy toward transparent, accessible, reliable, trusted and reproducible science. This is achieved by sharing how research and data collection are done so as to make research results accessible and standardized, created once and reused by many. This includes techniques, tools, technologies, and platforms should also be open source wherever possible.

In OS the outputs of the scientific process are considered to be a public good, thus wherever possible articles are published in open access (OA) journals, and research data are shared with the public and other scientists who may want to re-purpose those data in new work, or by people who want to verify the veracity of research results. Reporting COVID-19 Cases by normalizing an open by default approaches means that health scientists, population health experts and government officials make this part of their workflow (maintaining individual privacy of course), and by doing so decision makers beyond government, can scrutinize the results, leading to trust the results while also increasing data sharing.

What role does open science play in combating COVID-19?

In the early stages of the pandemic, knowing the genome provided crucial information to help scientists and researchers identify the origin of the outbreak, treat the infection, develop a diagnostic test and work on the vaccine. In other words, the easier—and quicker—researchers can produce, share and access scientific data, the quicker and the more informed is the collaborative response to the virus.

During the 2002-03 SARS outbreak it took five months to publish a full genome of the virus largely due to information blackouts and lack of data sharing. In contrast, the full genome of COVID-19 was published to an open-access platform nearly a month after the first patient was admitted to the hospital in Wuhan. This provided researchers around the world with a head start. Since OS policies have been operationalized during the pandemic, the resulting free flow of ideas in terms of biomedical research has accelerated (OECD).

The implementation of OS standards during COVID-19 has indeed been largely successful. OECD described how collaborative research and  thee global sharing of information reached unprecedented levels, for example:

  • In March 2020, 12 countries (including Canada) launched the Public Health Emergency COVID-19 Initiative at the level of Chief Science advisors, calling for open access to publications and machine-readable access to data related to COVID-19.
  • Open online platform Vivli offers an easy way to request anonymized data from clinical trials.
  • A COVID-19 Open Research Dataset [CORD-19] was developed that hosts 157,000 + scholarly articles about COVID-19 and related coronaviruses; 75,000 of which are full-text machine-readable data that can be used for AI and natural language processing.

These online, open-source platforms have supported rapid scientific COVID-19 research. OS, facilitated by standards, shared infrastructure and techniques, policies and licences, has been instrumental in the global fight against the pandemic.

Yet, despite the numerous successes, many challenges remain. For example, not all COVID-19 related health research and data adhere to the FAIR principles. FAIR principles are a standards approach which support the application of open science by making data Findable, Accessible, Interoperable, and Reusable. Failure to adhere to FAIR principles has led to an overall lack of communication and coordination during the pandemic. In Canada, data should also adhere to CARE principles, which address issues of Indigenous data governance with respect to Indigenous knowledge along with the OCAP Principles of the First Nation Information Governance Centre (FNIGC). More on this in the following section.

The reporting COVID-19 demographic data and reports in Canada to date falls short on standardized classifications in terms of demographics, as we discussed in an earlier blog post, which makes doing a comparative analysis difficult or impossible: for example, many countries define “recoveries” differently, and in Canada, since health is the jurisdictional responsibility of the provinces and territories, each report in their own way. Even though numerous official organizations publish COVID-19 and health related data, as open data databases or in open data portals, there remains an overall lack of interoperability, comparability and standards.

Where does Canada stand on Open Science?

Canada was implementing an open science framework before the pandemic as follows.

National Action Plan on Open Government

The Government of Canada recently published Canada’s 2018-2020 National Action Plan on Open Government, listing ten commitments to furthering the open government initiative. The plan asserts five commitments to implementing OS in Canada by the end of 2020, as seen below:

A screenshot showing a portion of Canada's 2018-2020 National Action Plan on  Open Government. The main issue addressed here is the difficulty for Canadians to access scientific research outputs: thus the commitments focus on making federal science, scientific data, and scientists themselves more accessible.

The OS portion of Canada’s 2018-2020 National Action Plan on Open Government. It aims to address the difficulty for Canadians to access scientific research: thus the commitments on making federal science, scientific data, and scientists themselves more accessible (Government of Canada, 2018).

The Action Plan addresses issues of accessibility and transparency of scientific research and outlines 5 commitments to amending these issues. These commitments include:

  1. Development of an OS roadmap,
  2. Providing an open access platform for publications,
  3. Raising awareness of federal scientists’ work,
  4. Promoting OS and soliciting feedback on stakeholder needs, and
  5. Measuring progress & benefits of the OS implementation.

Despite the comprehensiveness of the Roadmap (see below), Canada has not yet moved past the Action Plan’s second commitment—to provide a platform for Canadians to find and access open access (OA) publications from federal scientists—despite the projected March 2020 deadline. Also, at the time of writing, there is no federal open science platform or portal for users to access open science data in Canada even though there is an open data portal. The New Digital Research Infrastructure Organization (NDRIO) does show promise.

There are however some open data initiatives, such as the Federal Open Government and COVID-19 section on the Open Government Canada Portal.  Here Epidemiological and economic research data, with mathematical modeling reports, a map of cases and deaths by province, daily and weekly detailed epidemiological reports, and an ongoing dataset of COVID-19 cases, deaths, recoveries, and testing rates in Canada’s provinces and territories are made available. This is a significant improvement from the early days of reporting, as data journalist Kenyon Wallace discovered that on a daily basis, the Province of Ontario published new data but each time they did they overrode the previous day’s reports. His article and some work by Lauriault with the Ontario Open Government team resulted in changing that practice and raw data are now updated daily and reported. Open data is but one part of the OS process as we will see when we look at the FAIR principles.

Open Science Roadmap

The plan’s first commitment, to “develop a Canada Open Science Roadmap…” was completed and published in February 2020. The document provides ten recommendations made by Chief Science Advisor, Dr. Mona Nemer, to advance Canada’s OS initiatives. Like the policy brief by OECD, the roadmap is driven by the importance of trust among collaborators, inclusiveness of varying perspectives, and transparent processes throughout.

A screenshot of the cover of Canada’s Roadmap for Open Science (Government of Canada, 2020)

Canada’s Roadmap for Open Science (Government of Canada, 2020)

Most importantly, the Roadmap describes a commitment to developing an OS framework, including adopting the FAIR principles and “open by design and by default” specifications. The roadmap asserts Canada’s commitment to upholding these standards and policies via 10 recommendations:

10 recommendations made in the Roadmap for Open Science. Key points include the adoption of an OS framework in Canada, making federal scientific research outputs ‘open by default’, and implementing FAIR principles. (Government of Canada, 2020).

10 recommendations in the Roadmap for Open Science. Key points include the adoption of an OS framework, making federal scientific research outputs ‘open by default’, and implementing FAIR principles (Government of Canada, 2020).

Model Science Integrity Policy

Canada also has a Model Science Integrity Policy (MSIP) for the public service. The MSIP represents an internal commitment to integrity and accountability in science. Various mandates in the MSIP state that their purpose is to increase public trust in the credibility and reliability of government research and scientific activities, and ensure that research and scientific information are made available in keeping with the Government of Canada’s Directive on Open Government. The MSIP echoes Canada’s commitment to OS.

Indigenous Data Governance 

Finally, Canada has some commitment to supporting Indigenous rights to self-determination and data governance, but does not incorporate standards such as CARE principles which support OS  nor the OCAP Principles when it comes to Indigenous data governance. These extend the FAIR principles.

The Global Indigenous Data Alliance (GIDA) introduced the CARE principles to complement the FAIR principles in 2019. The CARE principles for Indigenous data governance were developed to address a lack of engagement between the open science movement and Indigenous rights and interests (GIDA, 2019).

The FAIR principles focus on data accessibility of data and sharing but fail to address power differences and the impact of colonialism experienced by Indigenous peoples and their right to exercise control and ownership of data about them and local and traditional knowledge. The CARE principles are crucial for the recognition and advancement of these rights as they encourage open science (and other ‘open’ movements) to “consider both people and purpose in their advocacy and pursuits” (GIDA, 2019). The CARE principles are contrasted with the FAIR principles in the below image from the GIDA website:

The CARE principles, which are “collective benefit, authority to control, responsibility, and ethics”, contrasted with the FAIR principles, which are “findable, accessible, interoperable, and reusable” (GIDA, 2019)

The CARE principles are “collective benefit, authority to control, responsibility, and ethics”, contrasted with the FAIR principles, which are “findable, accessible, interoperable, and reusable” (GIDA, 2019)

The OCAP Principles of Ownership, Control, Access and Possession are another set of important principles, that are a better fit in the Canadian Context.  Members of our project currently taking the Fundamentals of OCAP course and we hope to better incorporate these approaches in our work and in how we assess official reporting. Though Indigenous data governance and handling of Indigenous knowledge are not addressed in the Open Science Roadmap, the Data Strategy Roadmap for the Federal Public Service does demonstrate a federal approach to supporting Indigenous data strategies (see below):

Recommendation #8 from the Data Strategy Roadmap for the Federal Public Service which states Canada’s recognition of the Indigenous right to self-determination and data governance (Government of Canada, 2019)

Recommendation #8 from the Data Strategy Roadmap for the Federal Public Service which states Canada’s recognition of the Indigenous right to self-determination and data governance (Government of Canada, 2019)

Next Steps

Much progress has been made in terms of publishing, reporting and communicating data in the short time since COVID-19 began (though not without pressure from the media!). Open access to scientific research and public health reports have been helpful to facilitate the rapid response to the virus and keeping the public informed on how science informs governments actions. There is, however, much left to be done.

  1. Open Science should consider bias in data as well as invisibilities for example interdisciplinary work that helps paint the fuller picture of the impact of the virus. For example, interdisciplinary and intersectional approaches to data categories, including research based in critical race theory (CRT), Indigenous perspectives, socio-demographics and gig labour groups for example.
  2. Second, as suggested by the OECD, making COVID-19 data Findable, Accessible, Interoperable and Reusable is critical for a more effective rapid response. Lack of adherence to FAIR principles currently presents challenges to open science research.
  3. Finally, a meaningful Canadian OS framework should also incorporate standards for Indigenous Data Governance such as CARE Principles and OCAP Principles ensure respectful data practices are followed.

The Tracing COVID-19 Data team is in the process of developing a framework to assess official COVID-19 reporting in Canada to see if they comply with OS, FAIR, CARE, OCAP, and open-by-default at all levels of government. We will draw on Canada’s commitments OS and FAIR in – Canada’s 2018-2020 National Action Plan on Open Government, Open Science Roadmap, the Model Science Integrity Policy and the Open Data Charter.

Is Canada FAIR?

Stay tuned!


All official Federal, Provincial/Territorial and City public COVID-19 data reporting should be open data, open by design and by default, research should be published in open access (OA) Journals and should adhere to open science (OS) such as the FAIR principles , CARE Principles, OCAP Principles and the Open Data Charter.


Canadian Internet Policy and Public Interest Clinic. Open Data, Open Citizens? https://cippic.ca/en/open_governance/open_data_and_privacy

Centres for Disease Control and Prevention. (n.d.). SARS- Associated Coronavirus (SARS-CoV) Sequencing. https://www.cdc.gov/sars/lab/sequence.html

CTVNews. (2020). Project Pandemic: Reporting on COVID-19 in Canada. 

Federated Research Data Repository. (2018). FAIR Principles. 

Global Indigenous Data Alliance. (2019). CARE Principles for Indigenous Data Governance. 

Government of Canada. (2014). Directive on open government. 

Government of Canada. (May, 2016). Open by default and modern, easy to use formats. 

Government of Canada. (2017). Model policy on scientific integrity.

Government of Canada. (2018). Canada’s 2018-2020 National Action Plan on Open Government. https://open.canada.ca/en/content/canadas-2018-2020-national-action-plan-open-government#toc8

Government of Canada. (2018). Report to the Clerk of the Privy Council: A Data Strategy Roadmap for the Federal Public Service. https://www.canada.ca/content/dam/pco-bcp/documents/clk/Data_Strategy_Roadmap_ENG.pdf

Government of Canada. (2020). Coronavirus disease (COVID-19): Outbreak update.

Government of Canada. (2020). Office of the Chief Science Advisorhttps://www.ic.gc.ca/eic/site/063.nsf/eng/h_97646.html

Government of Canada. (2020). Open Government Portal.

Lauriault, T. (2020, April 17). Tracing COVID-19 Data: COVID-19 Demographic Reporting. Datalibre.

National Centre for Biotechnology Information. (2020). Public Health Emergency COVID-19 Initiative.

Open Data Charter. (n.d.). The International Open Data Charter.

Organisation for Economic Co-operation and Development. (2020, May 12). OECD Policy Responses to Coronavirus (COVID-19): Why open science is critical to combatting COVID-19.

Ford & Airhihenbuwa. (2010). The public health critical race methodology: Praxis for antiracism research. Science Direct.

Semantic Scholar. (2020). CORD-19: COVID-19 Open Research Dataset.

The Lancet. (January, 2020). Genomic characterization and epidemiology of 2019 novel coronavirus: implications for virus origins and receptor binding.

The National Academies of Science, Engineering, Medicine. (2018). Open Science by Design: Realizing a Vision for 21st Century Research. Chapter 1, Front Matter. 

The Star. (2020). Coronavirus & COVID-19 Data. https://www.thestar.com/coronavirus/data.html

Vivli. (2020).

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Article written by: Kit Chokly & Tracey P. Lauriault

The phrase “flatten the curve” has recently come into the vernacular to encourage slowing the spread of COVID-19. The concept comes from the Centre for Disease Control (CDC) report (p.18), where the curve renders the daily number of COVID-19 infections on the Y-axis and the day these are counted on X-axis of a line chart. A flatter curve visually depicts when the illness is under control, while a higher peak curve emerges when the health case system becomes overwhelmed and instances of the virus are high. Together we work to “flatten the curve” by following the recommendations of our Chief Medical Officers of Health.

This is not the only visual metaphor used to refer to COVID-19, however; the “second wave” has also been used to describe the spread of COVID-19. This term refers to another peak in the line chart that may appear as restrictions are lifted and cases spike as a result. By visualizing health data, we can see the spread of COVID-19 and set public health goals through the peaks and valleys of a line chart.

In addition to demonstrating a growing public awareness of the pandemic, the popularity of these phrases shows the importance of data visualization in understanding—and thus managing and communicating—the virus. Following this prevalence, many official sources now share live data to track and communicate COVID-19 data. John Hopkins University, for example, rendered COVID-19 datasets into the first global and US dashboards. Health Canada provides an interactive map of Canada featuring case counts and rates. Some city public health departments such as Ottawa Public Health have also launched dashboards to communicate the current status of local COVID-19 cases.

These dashboards get complex information across quickly and include many types of indicators. For instance, as seen below, the City of Ottawa dashboard includes key indicators such as the number of COVID-19 cases, hospitalization levels, and other case data. These are depicted in a cumulative curve of confirmed cases, their rate across time, bar charts of cases reported by age, and a pie chart that shows cases by gender. Visualizing these data in a dashboard makes it easy to see patterns and trends.

A screenshot of the city of Ottawa’s COVID-19 dashboard, which effectively condenses complex data on COVID-19 cases and outcomes into bite-sized visualizations (Ottawa Public Health, 2020).

A screenshot of the city of Ottawa’s COVID-19 dashboard, which effectively condenses complex data on COVID-19 cases and outcomes into bite-sized visualizations (Ottawa Public Health, 2020).

While the city and all the other public health agencies across Canada should be lauded for their dashboards, these, like all other demographic reporting systems, suffer from a lack of a nuanced and intersectional approach to collecting and reporting data. For example, this dashboard alongside those just mentioned do not report how COVID-19 affects people of low socio-economic status. They do not include how living in a densely populated area may also influence health outcomes. There is also little to no data on how COVID-19 may disproportionately affect racialized groups such as Indigenous, Black, and other people of colour. And most importantly, they do not demonstrate how all of these variables intersect. As Jamie Bartlett and Nathaniel Tkacz (2017) explain,

“Like all visualisations of data, dashboards necessarily distort the information that they are attempting to present neutrally by defining how a variable is to be understood and by excluding any data which isn’t compatible with this definition” (p. 15).

As Maggie Walter and Chris Andersen (2013) discuss in their book Indigenous Statistics, data dashboards—like all data and technological systems—are more than neutral numerics as they play,

“a powerful role in constituting reality through their underpinning methodologies by virtue of the social, cultural, and racial terrain in which they are conceived, collected, analyzed, and interpreted” (p. 9).

They also emphasize that there is a distinction to be made between groups of people being “statistical creations based on aggregated individual-level data, rather than ‘real world’ concrete groups” (Walter & Andersen, 2013, p.9).

It is not uncommon that dashboards miss these subtleties. We are only just coming to terms with the idea that data are not neutral objects, and that data dashboards are cultural artifacts. As such, they reflect the limitations of the normative values of the institutions that create them, of our health data collection systems, and of this very form of communication.

Overcoming these limitations means that we can improve the information environment of decision makers to target limited resources when and where they are needed most, while not reinforcing nor perpetuating inequality. Making things visible makes them actionable. Doing so also prevents the deep systemic societal problems that have led to such poor health outcomes for some during the pandemic. Below, we consider some ideas on how to re-think data and data dashboards by framing their creation with an intersectional design approach.

What is intersectionality?

Intersectionality is a term conceptualized by Black feminist legal scholar Kimberlé Crenshaw in 1989. The concept has earlier roots in Black feminism and builds on the work and struggles of Sojourner Truth, Ida B. Wells, Louise Thompson Patterson, and Audre Lorde (to name only a few). Intersectionality refers to the notion that different forms of oppression interact with and multiply each other, demonstrating their inseparability. Health scientists Chandra Ford and Collins Airhihenbuwa (2010) write:

“Intersectionality posits that social categories (e.g., race, gender) and the forms of social stratification that maintain them (e.g., racism, sexism) are interlocking, not discrete” (p. 1396).

These scholars and social justice leaders emphasize that rather than adding these co-occurring categories together in public health research, it is essential that the interactions between categories are also considered (Ford & Airhihenbuwa, 2010). As an example of intersectional public health, Ford and Airhihenbuwa (2010) describe the importance of considering race alongside gender and sexuality when approaching HIV risk behaviours because of the way “racism operates via gendered and sexualized proscriptions” (p. 1394).

How does intersectionality relate to the Tracing COVID-19 Data project?

Epidemiologist Greta Bauer (2014) describes how datasets about public health and marginalized populations tend to only examine a singular axis of oppression, even though intersectional data reporting can help reduce health inequalities.

One of the aims of the Tracing COVID-19 Data project is to identify these asymmetries in COVID-19 data reports, as well as rapidly and effectively mobilizing this knowledge and communicating our findings with decision makers. As demonstrated by the growing use of dashboards in reporting COVID-19 data, data visualization can be a useful communication tool. We thus ask:

How can we communicate COVID-19 data in a way that improves health outcomes for all? How can we use data visualization techniques to communicate intersectional issues effectively?

We aim to provide answers to questions such as these for those responsible for official COVID-19 reporting. Our hope is that our recommendations will lead to an intersectional approach to communicating COVID-19 impacts and health outcomes.

How have people tried to visualize intersectionality already?

Visual metaphors are useful to describe intersectionality. Crenshaw (1989) herself uses the metaphor of the traffic intersection, writing:

“Discrimination, like traffic through an intersection, may flow in one direction, and it may flow in another. If an accident happens in an intersection, it can be caused by cars traveling from any number of directions and, sometimes, from all of them. Similarly, if a Black woman is harmed because she is in the intersection, her injury could result from sex discrimination or race discrimination (p. 149).”

In another metaphor, provided by Black feminist scholar Patricia Hill Collins (1990) is the “matrix of domination”. Here, she uses a multi-level matrix to describe how oppression—and privilege—must be understood through an interlocking structural model (Hill Collins, 1990).

More recently, communications scholars Jenna Abetz and Julia Moore (2019) describe how visual metaphors for intersectionality often focus on centralizing difference and are often conceptualized as linear. They suggest the use of fractals—repeating and irregular geometric patterns—as a metaphor for the scalability and recursion of oppression (Abetz & Moore, 2019).

An example of a von Koch curve fractal from Abetz and Moore’s (2019) article, which they use as a metaphor to explore the scalability and recursion of oppression.

An example of a von Koch curve fractal from Abetz and Moore’s (2019) article, which they use as a metaphor to explore the scalability and recursion of oppression.

The issue with metaphors

While these visual metaphors are extremely useful to conceptualize intersectionality, Bauer (2014) points to the issue with their use in quantitative research. She writes:

“Interestingly, quantitative applications of intersectionality can be obfuscated by the predominance of mathematical-like language in intersectionality theory, though its use there is conceptual rather than strictly mathematical” (Bauer, 2014, p. 12).

While illustrations of traffic intersections, graphic matrices, and fractal patterns can be used to explain intersectionality, they do not easily map onto the visualization of quantitative data in a data dashboard and can actually obscure the meanings of these data. They may, however, be useful instruments to help model which data should be collected and rendered visually.

Feminist Data Visualization

To address this issue, data scientists Catherine D’Ignazio and Lauren Klein (2016) suggest applying feminist theory to data visualization. Rather than resign ourselves to the limitations of current dashboards, feminist data visualzation offers the possibility of “challeng[ing] the validity of a variety of binaristic and hierarchical configurations” (D’Ignazio & Klein, 2016, p. 1). This includes non-intersectional data analysis.

D’Ignazio and Klein (2016) thus suggest that data visualization begins with the way data are collected and organized—even before they are visualized.

This means starting with the evidence collected and finding the best way to get the stories they tell across. This could mean enlisting the help of metaphors; however, it is important to find the stories from the data first and then use metaphor to communicate them as effectively as possible.

To find and interpret these data stories, D’Ignazio and Klein (2016) offer six starting principles:

  1. Rethink binaries
  2. Embrace pluralism
  3. Examine Power and Aspire to Empowerment
  4. Consider Context
  5. Legitimize Embodiment and Affect
  6. Make Labor Visible

Finding and using intersectional data

Despite the lack of intersectional data in current COVID-19 dashboards, a number of organizations are already making efforts to find and use intersectional data. The Data Standards for the Identification and Monitoring of Systemic Racism, produced by the Ontario Anti-Racism Directorate, not only takes an intersectional approach to the collection of data and data characteristics, but uses it to identify and monitor systemic racism. These ideals align with the Research, Evaluation, Data Collection, and Ethics (REDE) Protocol for Black Populations in Canada Protocol (or the REDE4BlackLives Protocol for short), which also suggests that these data should be part of ongoing conversations in pre-existing communities.

This is not unlike the work of:

The cover of the First Nations Data Governance Strategy, produced by the FNIGC as a response to direction received from First Nations leadership.

The cover of the First Nations Data Governance Strategy, produced by the FNIGC as a response to direction received from First Nations leadership.

A graphic from the Global Indigenous Data Alliance (2019) encouraging the use of CARE principles to encourage Indigenous data sovereignty alongside Wilkinson et al’s (2016) FAIR open data principles.

A graphic from the Global Indigenous Data Alliance (2019) encouraging the use of CARE principles to encourage Indigenous data sovereignty alongside Wilkinson et al’s (2016) FAIR open data principles.

The Tracing COVID-19 Data project is working to bring together some of these datasets as they pertain to COVID-19. For example, with the help of Aidan Battley, we are looking into social models of disability and data such as the International Classification of Functioning, Disability and Health (ICF) by the World Health Organization (WHO). These models and data fall well into the intentions of this project, which is to encourage technological citizenship and a rights based approach to data during a crisis. Alongside collecting these data, visualization is key to reach our goals.

But how do we align these principles, protocols, standards, and practices? How might one model these in a population health data system? And how should these data be rendered visually? These are challenges worth pursuing, as lives are quite literally on the line.

What efforts might be useful to adapt for the Tracing COVID-19 Data project?

Following these principles, protocols, standards and practices, the Tracing COVID-19 Data project is critically thinking about data and data systems. We are currently collecting data from all over the country, including data on the way location, age, ability, race, Indigeneity, gender, and income may intersect with COVID-19 outcomes. From there, we aim to develop a series of rapid and archivable visualizations and blog posts to communicate our findings in ways best suited to both researchers and the communities they describe, as well as provide recommendations to decision makers on how to improve their data dashboards and other data visualizations techniques.

We are also mindful of becoming flexible and adaptable to new solutions (and issues) as they arise. The work of visualizing the intersectional impacts of COVID-19 is important, but loses its value if it becomes too brittle to be used effectively. We have already heard words of caution from racialized communities who rightfully fear becoming further stigmatized by being described through COVID-19 data. Listening to and working with the communities who are being multiply impacted by COVID-19 is critical to the success of this project. For this work to truly be intersectional, it is essential that we are all on board to listen and work together.

Slides from a presentation given on this topic (Sept 22, 2020)

References, Links, & Resources

Abetz, J., & Moore, J. (2018). Visualizing intersectionality through a fractal metaphor. In J. Dunn & J. Manning (Eds.), Transgressing Feminist Theory And Discourse (1st ed., pp. 31–43). Routledge. https://doi.org/10.4324/9781351209793-3

Bartlett, J., & Tkacz, N. (2017). Governance by Dashboard. https://core.ac.uk/download/pdf/80851285.pdf

Bauer, G. R. (2014). Incorporating intersectionality theory into population health research methodology: Challenges and the potential to advance health equity. Social Science & Medicine, 110, 10–17. https://doi.org/10.1016/j.socscimed.2014.03.022

Centre for Disease Control. (2007). Interim Pre-pandemic Planning Guidance: Community Strategy for Pandemic Influenza Mitigation in the United States. 97. https://www.cdc.gov/flu/pandemic-resources/pdf/community_mitigation-sm.pdf

Columbia Law School. (2017, June 8). Kimberlé Crenshaw on Intersectionality, More than Two Decades Later. https://www.law.columbia.edu/news/archive/kimberle-crenshaw-intersectionality-more-two-decades-later

Crenshaw, K. (1989). Demarginalizing the Intersection of Race and Sex: A Black Feminist Critique of Antidiscrimination Doctrine, Feminist Theory and Antiracist Politics. University of Chicago Legal Forum, 1989(1), 31. https://chicagounbound.uchicago.edu/cgi/viewcontent.cgi?article=1052&context=uclf

D’Ignazio, C., & Klein, L. F. (2016). Feminist Data Visualization. 5. http://www.kanarinka.com/wp-content/uploads/2015/07/IEEE_Feminist_Data_Visualization.pdf

First Nations Information Governance Centre. (2020). The First Nations Information Governance Centre. https://fnigc.ca/index.php

Ford, C. L., & Airhihenbuwa, C. O. (2010). The public health critical race methodology: Praxis for antiracism research. Social Science & Medicine, 71(8), 1390–1398. https://doi.org/10.1016/j.socscimed.2010.07.030

Fundamentals of OCAP®. (2020). The First Nations Information Governance Centre. https://fnigc.ca/training/fundamentals-ocap.html

Gilyard, K. (2017). Louise Thompson Patterson: A Life of Struggle for Justice. Duke University Press. https://www.dukeupress.edu/louise-thompson-patterson

Government of Canada. (2020, July 8). Coronavirus disease (COVID-19): Outbreak update. https://www.canada.ca/en/public-health/services/diseases/2019-novel-coronavirus-infection.html

Government of Ontario. (2016, June 28). Anti-Racism Directorate. https://www.ontario.ca/page/anti-racism-directorate

Government of Ontario. (2019, August 27). Data Standards for the Identification and Monitoring of Systemic Racism. https://www.ontario.ca/document/data-standards-identification-and-monitoring-systemic-racism

Government of Ontario. (2020, July 8). COVID-19 (coronavirus) in Ontario. https://covid-19.ontario.ca/

Greenwood, F., Howarth, C., Poole, D. E., Raymond, N. A., & Scarnecchia, D. P. (2017). The Signal Code: A human rights approach to information during crisis. Harvard Humanitarian Initiative. https://hhi.harvard.edu/publications/signal-code-human-rights-approach-information-during-crisis#:~:text=The%20Signal%20Code%20is%20the,have%20to%20information%20during%20disasters.

Hill Collins, P. (1990). Black feminist thought: Knowledge, consciousness, and the politics of empowerment. Unwin Hyman. http://www.hartford-hwp.com/archives/45a/252.html

John Hopkins University. (2020, July 8). COVID-19 Map—Johns Hopkins Coronavirus Resource Center. https://coronavirus.jhu.edu/map.html

Lauriault, T. (2020, June 1). Tracing COVID-19 Data: Data and Technological Citizenship during the COVID-19 Pandemic. Datalibre. Retrieved 8 July 2020, from https://www.datalibre.ca/2020/06/01/tracing-covid-19-data-data-and-technological-citizenship-during-the-covid-19-pandemic/

Lorde, A. (1981). The Uses of Anger: Women Responding to Racism. BlackPast. https://www.blackpast.org/african-american-history/speeches-african-american-history/1981-audre-lorde-uses-anger-women-responding-racism/

National Underground Railroad Freedom Center. (n.d.). Ida B. Wells. Retrieved 8 July 2020, from https://freedomcenter.org/content/ida-b-wells

Ottawa Public Health. (2020, July 8). Daily COVID-19 Dashboard. https://www.ottawapublichealth.ca/en/reports-research-and-statistics/daily-covid19-dashboard.aspx

Podell, L. (n.d.). The Sojourner Truth Project. The Sojourner Truth Project. Retrieved 8 July 2020, from https://www.thesojournertruthproject.com

Research Data Alliance International Indigenous Data Sovereignty Interest Group. (2019). CARE Principles for Indigenous Data Governance. The Global Indigenous Data Alliance. https://static1.squarespace.com/static/5d3799de845604000199cd24/t/5da9f4479ecab221ce848fb2/1571419335217/CARE+Principles_One+Pagers+FINAL_Oct_17_2019.pdf

The First Nations Information Governance Centre. (2020). A First Nations Data Governance Strategy. https://fnigc.inlibro.net/cgi-bin/koha/opac-retrieve-file.pl?id=9c677f3dcf8adbf18fcda96c6244c459

The Protocol: REDE4BlackLives. (n.d.). REDE4BlackLives. Retrieved 8 July 2020, from https://rede4blacklives.com/the-protocol/

Walter, M. (2013). Indigenous statistics: A quantitative research methodology. Left Coast Press. https://www.routledge.com/Indigenous-Statistics-A-Quantitative-Research-Methodology/Walter-Andersen/p/book/9781611322934

Wilkinson, M. D., Dumontier, M., Aalbersberg, Ij. J., Appleton, G., Axton, M., Baak, A., Blomberg, N., Boiten, J.-W., da Silva Santos, L. B., Bourne, P. E., Bouwman, J., Brookes, A. J., Clark, T., Crosas, M., Dillo, I., Dumon, O., Edmunds, S., Evelo, C. T., Finkers, R., … Mons, B. (2016). The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data, 3(1), 160018. https://doi.org/10.1038/sdata.2016.18

World Health Organization. (2018, March 2). International Classification of Functioning, Disability and Health (ICF). https://www.who.int/classifications/icf/en/

World Health Organization. (2020). https://www.who.int

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We were invited today to share the work we are doing on the Tracing COVID-19 Data project to the Ottawa Local Immigration Partnership (OLIP) Health and Wellbeing Sector Table Meeting. This is an amazing group of dedicated actors from the Social Sector, the City, Health Sector, Education Sector, Police and Equity seeking groups that work toward the promotion of equality in Ottawa.  One of their Theory of Change Domain Areas is Equity Data, and you can read more about OLIP’s work here.

You can access the slides here.

Screenshot (77)


TracingCOVIDbanners-08The following data and information were collected and analyzed by Tracey P. Lauriault, and Sam Shields a recent Carleton University Critical Data Studies graduate.

We set out to answer a very simple question inspired by a Twitter stream calling for COVID-19 reporting to include Indigenous, Black and Racialized characteristics. The following guided our activities:

  • What kind of demographic data are reported in official COVID19 reports?

On Thursday April 16, 2020 we spent the day searching the content of official government COVID-19 reporting sites. We compiled our data into a Google Spreadsheet, conferred over Skype, chatted in FB, and verified each other’s work. Official COVID-19 reporting dynamically changes as the pandemic evolves, and as institutions collect more data and build the capacity to report, they report more and they do so in a better way. I also consult experts in my network who comment and suggest resources. We will take another look next week to see if anything has changed. The following were our data sources

  1. British Columbia: COVID Dashboard & BCCCD PHSA Surveillance Report (15/04/2020)
  2. Yukon: Information about COVID-19
  3. Alberta: COVID-19 in Alberta
  4. North West Territories: Coronavirus Disease (COVID-19)
  5. Saskatchewan: Cases and Risk of COVID-19 in Saskatchewan
  6. Manitoba: COVID-19 Updates
  7. Nunavut: COVID-19 (Novel Coronavirus)
  8. Ontario: The 2019 Novel Coronavirus (COVID-19) Status of cases in Ontario & Daily Epidemiologic Summary (15/04/2020)
  9. Québec: Données COVID-19 au Québec & Situation du coronavirus (COVID-19) au Québec
  10. New Brunswick: COVID-19 Testing by the Numbers
  11. Prince Edward Island: PEI COVID-19 Testing Data
  12. Nova Scotia: Novel coronaviris (COVID-19) cases in Nova Scotia: data visualization
  13. Newfoundland: Newfoundland and Labrador Pandemic Update Data Hub
  14. Federal: PHAC Coronavirus disease (COVID-19): Outbreak update & Full Daily Epidemiology Update (April 16, 2020)

We found an incredible amount of information and overall, each province, territory and the Federal government make their data readily available and these are disseminated in charts, tables, maps, and dynamic dashboards and in daily surveillance reports. The data and indicators are explained, and data sources are generally provided.

In terms official COVID-19 reporting, there was very little reporting cases and outcomes with demographic variables and when there was, it is not standardized, making it difficult to do any national comparative analysis.  Below is what we found.

1. Age

  • COVID-19 Cases by Age were reported by all provinces and the Federal Government. Age was not reported by all 3 Territories.
  • Those who did report, provided case counts and some percentages.
  • Only British Columbia, Alberta and Quebec reported Deaths by age groups.
  • Quebec reports age in 4 different ways.
  • There are no Age Range Reporting standards, and this impedes comparability.

The following is how COVID-19 Age data are reported, we ordered the results by similar reporting styles.

  • British Columbia: <10, 10-19, 20-29, 30-39, 40-49, 50-59, 60-69, 70-79, 80-89, 90+, Unknown
  • New Brunswick: <10, 10-19, 20-29, 30-39, 40-49, 50-59, 60-69, 70-79, 80-80, 90+
  • Manitoba: 0-9, 10-19, 20-29, 30-39, 40-49, 50-59, 60-69, 70-79, 80-89, 90-99, 100+
  • Quebec: 0-9, 10-19, 20-29, 30-39, 40-49, 50-59, 60-69, 70-79, 80-89, 90+, Unknown
  •                 0-29, 30-39, 40-49, 50-59, 60-69, 70-79, 80-89, 90+
  •                 30-49, 50-69, 70-79, 80-89, 90+
  •                 <30, 30-39, 40-49, 50-59, 60-69, 70-79, 80-89, 90, Unknown
  • Alberta: <1, 1-4, 5-9, 10-19, 20-29, 30-39 ,40-49, 50-59, 60-69, 70-79, 80+
  • Saskatchewan: <19, 20-44, 45-65, 65+
  • Ontario: <19, 20-39, 40-59, 60-79, 80+
  • Federal: 19, 20-29, 30-39, 40-49, 50-59, 60-69, 70-79, 80+
  • Nova Scotia: 0-19, 20-44, 45-64, 65+
  • PEI: <20, 20-39, 40-59, 60-79, 80+
  • Newfoundland: <20, 20-39, 40-49, 50-59, 60-69, 70+
  • Yukon:  No Reporting by Age
  • North West Territories: No Reporting By Age
  • Nunavut: No Reporting By Age

Age range variable reporting recommendations:

a) Standardize age ranges reporting systems across jurisdictions to enable comparison.

b) Social-determinant of health variables, such as occupation, income, the type of dwelling a person lives in, where one lives, are variables being reported as being related to COVID-19. The Census reports age by quintile although it start at 0-14, in Canada vital statistics are reported by age quintile and the World Health Organization (WHO) also reports by quintile. Linking to other aggregated demographic, health and vital statistical data can inform the planning, and the managing of health outcomes.

2. Sex

  •  Sex is Not reported as a COVID-19 attribute, by 4 Canadian jurisdictions, namely the Territories and  Newfoundland and Labrador.
  • For jurisdictions that do report COVID-19 data by sex, only binary classifications are used, Female and Male.
  • Only British Columbia, Alberta and Manitoba report Sex and Age as attributes.
  • Only Quebec and The Federal Government report Sex and Death.

Sex Variable Reporting Recommendations:

a) It is advisable to report COVID-19 indicators by sex such as Female, Male and Gender Diverse.

b) Sex disaggregated data are important in terms of informing testing; health interventions and it is associated with health outcomes. Knowing can inform planning.

c) Reporting age and sex is important as these are distinguishing characteristics in vital statistics, health, wellbeing, for longevity and death rates.  Also, reports suggest that the virus affects men more negatively than it does women, especially older men. In terms of the labour force and COVID-19, nurses, doctors, elder care and home care professionals, those who work with people who live in group homes for the disabled and provide home care for these people, and people who clean these places tend to be women. Higher numbers of women are becoming afflicted by COVID-19 in Canada and this may be associated with their occupations. Age and sex are standard labour force statistical variables and reporting these attributes with COVID-19 will inform if health outcomes are related to those attributes.

3. Labour Classification

  • In official COVID-19 reporting, only the Provinces of Saskatchewan and Quebec reported any labour category and respectively they reported Case Counts for Health Care Workers for Saskatchewan and Cases Count and Death Count of Staff in hospitals and long-term care homes for Quebec.

Labour Force Reporting Recommendations:

a) Canadian Labour Forces Characteristics such as employed full or part-time, and the North American Industry Classification System and National Occupation Classification (NOC) system are standardized. For example, see the NAICS Health Care and Social Services or the classification and search for cleaner in NOCS.

b) The Canadian Institute for Health Information (CIHI) health workforce database includes standardized job classifications and data tables by job classification. They also have methodological guides comparing provincial systems. Harmonizing classifications across the provinces and the territories would go a long way to facilitating comparable analysis.

4. Indigenous, Black and Racialized People

  • No official government COVID-19 sites report data by any of these groups.
  • Race and ethnicity may or may not biologically predispose people to COVID-19 health outcomes.  We are assuming that these data are being tracked but are not reported as there is a concern about how to report these data.
  • Indigenous, Black and Racialized people may also have preexisting health conditions that are socially and economically determined, and these preexisting conditions may disproportionally affect this group more than others. Furthermore, reports suggest that Indigenous, Black and Racialized People have been infected more than others, and their health outcomes are more dire. Evidence informed decisions can lead to better outcomes for some groups, reporting the numbers can advance better and more targeted practices in community, hospital and in our cities.

Recommendation on the Reporting with Indigenous, Black and Racialized People categories:

a) The Province of Ontario Anti-Racism Directorate publishes a Data Standards for the Identification and Monitoring of Systemic Racism that includes

“guidance for race-based data collection for government and other public sector organizations, including steps to follow for data collection, management and use”.

Table 1. Valid Values for Race Categories on P.26 provides a useful classification system.  The Standard also includes protocols for the collection of self reported or observed data.

b) First Nation, Metis and Inuit in Canada may be collecting these data in their communities.  I will consult to see if that is the case and report back.

Final Remarks:

Health outcomes are intersectional, and age, sex, workforce and equity data provided additional insight about who is being affected, and knowing who and where can inform decisions about determinants of health, testing, improvement of health outcomes and planning. We have provided some insight in this post, about what is being reported and provided some recommendations. We will provide updates as more information is collected. We hope you find this useful and we welcome your comments and suggestions by email: tracey.lauriault@carleton.ca or on Twitter @TraceyLauriault.



Reporting is becoming more sophisticated. The BC Centre for Disease Control (BCCCD) went from this landing page on the 13 of April, 3 days ago with data, maps, and charts as images on the page.


To this page today 16 of April and data are now reported in an ESRI dashboard, and some data available for download! I think it is easier to read. I hope they will continue to report their excellent Surveillance Reports, here is an example from April 15, 2020. You can access those reports at the bottom of the landing page. What is great about the dashboard is that it is a collaboration between a number of Provincial Agencies BCCDC, PHSA, B.C. Ministry of Health and GeoBC Production. Below the image I have also pasted what they include on their Terms of Use, Disclaimer and Limitations of Liability page from the Dashboard.  The one issue with the dashboard, is you cannot download or link to specific pages.


Below I copied and pasted the information directly from the Dashboard at 9:45 AM EST, 16 April 2020. It is useful to have this all in one place, including access to data, data sources and notes about the indicators. This comes from the Dashboard, and unfortunately I cannot hyperlink directly to this information.

Terms of use, disclaimer and limitations of liability

Although every effort has been made to provide accurate information, the Province of British Columbia, including the British Columbia Centre for Disease Control, the Provincial Health Services Authority and the British Columbia Ministry of Health makes no representation or warranties regarding the accuracy of the information in the dashboard and the associated data, nor will it accept responsibility for errors or omissions. Data may not reflect the current situation, and therefore should only be used for reference purposes. Access to and/or content of this dashboard and associated data may be suspended, discontinued, or altered, in part or in whole, at any time, for any reason, with or without prior notice, at the discretion of the Province of British Columbia.

Anyone using this information does so at his or her own risk, and by using such information agrees to indemnify the Province of British Columbia, including the British Columbia Centre for Disease Control, the Provincial Health Services Authority and the British Columbia Ministry of Health and its content providers from any and all liability, loss, injury, damages, costs and expenses (including legal fees and expenses) arising from such person’s use of the information on this website.

BCCDC/PHSA/B.C. Ministry of Health data sources are available at the links below:

Dashboard Usage Tips:

  • Hover over charts to see additional information.
  • Click the top right corner of any chart/window to make it full screen. Click again to return to the dashboard view.

Data Sources:

  • Case Details and Laboratory Information Data are updated daily Monday through Friday at 5:00 pm.
  • Data on cases is collected by Health Authorities during public health follow-up.
  • Confirmed cases include laboratory positive cases.
  • Laboratory data is supplied by the B.C. Centre for Disease Control Public Health Laboratory; tests performed for other provinces have been excluded.
  • Data on intensive care unit (ICU) admissions is provided by the PHSA Critical Care Working Group.
  • Test and case values may differ between amalgamated Health Authorities and B.C. as site locations are confirmed.

Data Over Time:

  • The number of laboratory tests performed and positivity rate over time are reported by the date of test result. On March 16, testing recommendations changed to focus on hospitalized patients, healthcare workers, long term care facility staff and residents, and those part of a cluster or outbreak who are experiencing respiratory symptoms. The current day is excluded from all laboratory indicators.
  • The number of new cases over time are reported by the date they are notified to public health.

Epidemiologic Indicators:

  • Cases are considered recovered after two lab-confirmed negative swabs taken 24 hours apart or when removed from isolation 10 days after symptom onset.
  • New cases are those reported daily in the PHO press briefing and reflect the difference in counts between one day and the next as of 10:00 am. This may not be equal to the number of cases reported by day, as cases reported prior to 10:00 am would have been included as New Cases in the previous day’s count. Because of the 10:00 am cut-off, the most recent day in time series graphs may contain only partial information. On Mondays, the number of new cases includes the number of new cases from Saturday and Sunday.
  • ICU values include the number of COVID-19 patients in all critical care beds (e.g., intensive care units; high acuity units; and other surge critical care spaces as they become available and/or required).

Laboratory Indicators:

  • Total tests represent the cumulative number of COVID-19 tests since testing began mid-January. Only tests for residents of B.C. are included.
  • New tests represent the number of COVID-19 tests performed in the 24 hour period prior to date of the dashboard update.
  • COVID-19 positivity rate is calculated as the number of positive specimens that day/total number of specimens tested (positive, negative, and indeterminate) that day.
  • Turn-around time is calculated as the daily average time (in hours) between specimen collection and report of a test result. Turn-around time includes the time to ship specimens to the lab; patients who live farther away are expected to have slightly longer average turn around times.
  • The rate of COVID-19 testing is defined as the cumulative number of people tested for COVID-19/BC population x 1,000,000 population. B.C. and Canadian rates are obtained from the Public Health Agency of Canada’s Daily Epidemiologic update site.

Health Authority Assignment:

  • Health Authority is assigned by place of residence; when not available, by location of the provider ordering the lab test.

Please direct questions and feedback to the BCCDC: Admininfo@bccdc.ca

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TracingCOVIDbanners-08It is very odd that national health organizations are not reporting COVID-19 cases aggregated into health regions even though provinces and territories are mostly reporting them in that way. And where is the national health framework datasets?

Framework data are a “set of continuous and fully integrated geospatial data that provide context and reference information for the country. Framework data are expected to be widely used and generally applicable, either underpinning or enabling geospatial applications” P.7.

Federal Electoral Districts for example, are the official framework data for Elections Canada and these data are updated for each election.  They are used to administer elections, report the results of exit polls during the elections, and show the results after an election.  Framework data are available in multiple formats as well as in cartographic or mapping products for Geographic Information Systems (GIS) such as ESRI, MapInfo or Tableau (Shapefiles), in KML formats for GoogleMaps, and in standardized online mapping GML Formats which also happens to also be a Treasury Board Secretariat of Standard for Geospatial Data. Election result data are aggregated into these framework data along with other socio-economic data, and once these data are mapped we can compare and can tell a more nuanced local, regional and national story, we can see patterns across the country.  The benefit of framework data are many, what is also great is they are created once by an authoritative source, they are updated and reliable, they are used many times, they are open data and everyone knows where to get them.

Considering that health care spending is one of the largest expenditures we have as a nation state, and it would be expected that in an era of accountability and transparency and where outcomes based management is the norm, it is astonishing that health data including its social determinants data are not disseminated in this way.  Yes, there are privacy issues, but we are capable of addressing those with the Census and Elections, which means we can also do so for health. We need to have an evidence based conversation about population health now more than ever, and we will need these data to tell a socio-economic story as well. Could we have done better? Who is doing great and why and who is not doing so great and why, what can we learn and what is the remedy?

Numerous useful and insightful interactive maps were published after the elections (CBC, CTV, Macleans, ESRI and many others), and these generated much discussion, people could see the results, they could situate themselves, they could see what friends and family in other places were experiencing.  Analysts and policy makers also had what they needed to understand and plan a new context. This is what democratic evidence based data journalism and policy making is all aboutt!

Natural Resources Canada is normally the producer of Canada’s framework data but it does not produce a health region framework dataset for Canada.  Arguably, these data would not only be useful during a pandemic, but also for administering and reporting health associated with natural resources such as allergies in the spring and fall, food insecurity, health and farming, or health after a natural disaster such as flooding and fires.  They data would also be useful to see where money is spent providing Canadians with the evidence they require to advocate for change.

So why no national heath reporting by their administrative boundaries and where is the health region framework dataset?

National Health Reporting Canada:

Virihealth.com and ESRI Canada produced the the first National ge0-COVID-19 reporting:





Federal Government:

Canada as a federation has jurisdictional divisions of power, and one of those jurisdictional  divides is health. We have the Canada Health Care Act (CHA) that

“establishes criteria and conditions related to insured health services and extended health care services that the provinces and territories must fulfill to receive the full federal cash contribution under the Canada Health Transfer (CHT)”.

The Canada Health Transfer (CHT) provides long-term predictable funding for health care, on a per capital basis and

“supports the principles of the Canada Health Act which are: universality; comprehensiveness; portability; accessibility; and, public administration”.

The provinces and territories receive cash transfers to deliver health care to Canadians and health care data reporting is done by the each province and territory separately. This alone justifies the creation of a national health region framework dataset. Which organization should be responsible for it?

There are three main organizations which are part of the Canada Health Portfolio  that currently report official COVID-19 cases. At the moment, they do not publish COVID-19 case data by health regions.

Health Canada “is the Federal department responsible for helping Canadians maintain and improve their health, while respecting individual choices and circumstances.” Health Canada is an official and authoritative national source of COVID-19 data and it publishes the Coronavirus disease (COVID-19): Outbreak update. Reporting includes an interactive map and a line graph of data by Province and Territory.



Public Health Agency of Canada (PHAC) promotes and protects the health of Canadians through leadership, partnership, innovation and action in public health and it does so by: Promoting health; Preventing and controlling chronic diseases and injuries; Preventing and controlling infectious diseases; Preparing for and responding to public health emergencies; Serving as a central point for sharing Canada’s expertise with the rest of the world; Applying international research and development to Canada’s public health programs; and Strengthening intergovernmental collaboration on public health and facilitate national approaches to public health policy and planning. PHAC now disseminates an excellent interactive dashboard entitled the National Epidemiological Summary of COVID-19 Cases in Canada. Their data sources are: Public Health Agency of Canada, Surveillance and Risk Assessment, Epidemiology update; Natural Resources Canada – Grey basemap with Credit: COVID-19 Situational Awareness tiger team Powered by ESRI-Canada and COVID-19 Canadian Geostatistical Platform, a collaboration between Public Health Agency of Canada, Statistics Canada and Natural Resources Canada.



Canadian Institute for Health Research (CIHR) is the Government of Canada’s health research investment agency and its mandate is to “excel, according to internationally accepted standards of scientific excellence, in the creation of new knowledge and its translation into improved health for Canadians, more effective health services and products and a strengthened Canadian health care system.” Although a research funding organization, CIHR could publish a national framework dataset of health units to help researchers in Canada and to also to disseminate the findings of research either about COVID-19 or any other research according to those administrative boundaries. (Update 07/04/2020 CIHR does not have a framework data file)

A national non-governmental organization, the Canadian Institute for Health Information (CIHI) also disseminates national comparative health data, mostly about the administration of health and it would make sense for them to also publish data by health units and to have such a framework dataset. CIHI is an independent, not-for-profit organization that provides essential information on Canada’s health system and the health of Canadians. (Update 07/04/2020 CIHI does not have a framework data file). CIHI’s mandate is

“to deliver comparable and actionable information to accelerate improvements in health care, health system performance and population health across the continuum of care”.

Natural Resources Canada is the producer of most of Canada’s Framework data, and it could with the help of the Canadian Council on Geomatics Provincial and Territorial Accord could create this framework file and this was discussed at the 4th Annual SDI Summit meetings hosted in Quebec City in the Fall of 2019.

Statistics Canada produces Provincial and Territorial Health Geographies and it does seem to have a national GIS Health Regions: Boundaries and Correspondence with Census Geography file for 2018, and if that is the case, why are health geographies not reported by these boundaries? (Update 07/04/2020 StatCan has a 2018 GIS national health geography file).  Here is a PDF version of the 2018 map.



Provincial and Territorial Official COVID-19 Case Reports and health geographies:

Below I have compiled a list of official COVID-19 Case reporting by province and territory, and when I could find them, I included a link to health administration geographies. That does not mean that data are reported in maps, but data are generally tabulated according to health administration geographies.


British Columbia

Manitoba (Updated RHA and Map info. 07/04/2020)

Newfoundland and Labrador (Updated RHA and Map info. 07/04/2020)

New Brunswick (Updated RHA and Map info. 07/04/2020)

North West Territories

Nova Scotia



Prince Edward Island (Updated Health PEI info. 07/04/2020)

Quebec (Updated Map info 08/04/2020)


Yukon (Updated Health Region info. 07/04/2020)

I have emailed each of the Provincial and Territorial governments to confirm that I have the latest heath geography framework data.  I have received updates from Yukon, Quebec,  PEI, New Brunswick, and Manitoba, and have updated map data accordingly. I have also received correspondence from Statistics Canada, and CIHI.

For the moment ESRI Canada and some of the Provinces and Territories are reporting Official COVID-19 Cases by health region geographies.  Why aren’t Health Canada and the Public Health Agency of Canada doing so?  And where is the National Health Region Framework Data file?

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